Four questions about your task, and an honest answer about whether it needs a model at all.
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"AI" is the word that gets funded, so people reach for an LLM on problems a plain function would solve faster, cheaper, and more reliably. The honest default is the other way around: use code until the task genuinely needs language understanding or judgment you can't script — and even then, prefer a hybrid where deterministic code does the load-bearing work and the model only handles the fuzzy edge. A model you don't need is just a slow, expensive, non-deterministic function with a bigger bill.
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Plain code — no model needed
A function, a rule, a query — written once, runs the same forever. It's free, instant, testable, and never hallucinates. If a clear rule on clean input produces the answer, an LLM here is a liability dressed as a feature.
Elige esto cuando
A rule or calculation determines the right answer
The input is already structured and predictable
You want it fast, cheap, deterministic, and easy to test
Compensaciones
Can't handle inputs you didn't anticipate or messy human language
You have to actually write and maintain the logic
Add an LLM later only if a real fuzzy edge shows up
Hybrid — code core, model at the edges
The sophisticated middle, and more often the right answer than either extreme. Deterministic code does the load-bearing work and owns the source of truth; the model handles only the fuzzy part — reading messy input, drafting, proposing — while your rules ground and verify it. You get the model's flexibility without betting correctness on it.
Elige esto cuando
The logic is scriptable, but the input arrives as messy language
It needs judgment, yet there's a structured spine or a checkable answer
You want the model's reach but refuse to trust it unchecked
Compensaciones
More moving parts than either pure approach — two layers to design
You have to draw the line: what the code owns vs. what the model is allowed to decide
The model step still needs grounding and verification, not blind trust
An LLM — this is genuinely its job
Messy free-form language in, open-ended judgment out, with no single right answer to compute. Summarizing, drafting, classifying fuzzy intent, answering in natural language — this is what only a model does well, and pretending otherwise means rebuilding a worse one by hand.
Elige esto cuando
The task is open-ended language or judgment, not a rule
The input is messy, varied human text you can't fully anticipate
There's no single correct output to check against
Compensaciones
Costs money and latency on every call — watch it at scale
Non-deterministic and hard to verify — wrap it in evals and guardrails
Ground it in a real source of truth; never let an unchecked answer touch anything that matters